{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "api_key_project = \"\" #MK's Key\n",
    "#api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 1  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '100'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_experts'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Tracking run with wandb version 0.15.11"
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       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_091810-498mprxe</code>"
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     "data": {
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       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/498mprxe' target=\"_blank\">finetune_chetGPT_hatespeech_experts</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
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     "metadata": {},
     "output_type": "display_data"
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       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/498mprxe' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/498mprxe</a>"
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/498mprxe?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
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       "<wandb.sdk.wandb_run.Run at 0x18a187c77c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_1__task_1_train_set_100 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_1__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_1__task_1_train_set_100 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_1__task_1_train_set_100\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 879.8832487309645, 869.0\n",
      "p5 / p95: 853.0, 920.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.286802030456853, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346674 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1733370 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 848, 997\n",
      "mean / median: 879.23, 867.5\n",
      "p5 / p95: 853.9, 918.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 4.99, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~87923 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~439615 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 434597\n",
      "\n",
      "#### Estimated cost: 3.48 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-ioGxiXfTcw04AN8IU7ThgrRd\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich-ipz-phd:ds-1-t-1-trn-100:B5q1qCbY has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 10/25: training loss=0.08, validation loss=0.33, full validation loss=0.14\n",
      "Step 11/25: training loss=0.04, validation loss=0.05\n",
      "Step 12/25: training loss=0.10, validation loss=0.13\n",
      "Step 13/25: training loss=0.05, validation loss=0.17\n",
      "Step 14/25: training loss=0.07, validation loss=0.21\n",
      "Step 15/25: training loss=0.05, validation loss=0.13, full validation loss=0.16\n",
      "Step 16/25: training loss=0.02, validation loss=0.07\n",
      "Step 17/25: training loss=0.07, validation loss=0.08\n",
      "Step 18/25: training loss=0.03, validation loss=0.18\n",
      "Step 19/25: training loss=0.02, validation loss=0.15\n",
      "Step 20/25: training loss=0.01, validation loss=0.10, full validation loss=0.14\n",
      "Step 21/25: training loss=0.05, validation loss=0.22\n",
      "Step 22/25: training loss=0.01, validation loss=0.07\n",
      "Step 23/25: training loss=0.02, validation loss=0.07\n",
      "Step 24/25: training loss=0.02, validation loss=0.05\n",
      "Step 25/25: training loss=0.00, validation loss=0.08, full validation loss=0.17\n",
      "Checkpoint created at step 15\n",
      "Checkpoint created at step 20\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [04:35<00:00,  1.43it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.6439024390243903,\n",
      "                 'precision': 0.6947368421052632,\n",
      "                 'recall': 0.6,\n",
      "                 'support': 110},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.812933025404157,\n",
      "                       'precision': 0.8380952380952381,\n",
      "                       'recall': 0.7892376681614349,\n",
      "                       'support': 223},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.52,\n",
      "                  'precision': 0.43820224719101125,\n",
      "                  'recall': 0.639344262295082,\n",
      "                  'support': 61},\n",
      " 'accuracy': 0.7131979695431472,\n",
      " 'macro avg': {'f1-score': 0.6589451548095158,\n",
      "               'precision': 0.6570114424638375,\n",
      "               'recall': 0.6761939768188391,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.7203891699436801,\n",
      "                  'precision': 0.7361589538209868,\n",
      "                  'recall': 0.7131979695431472,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_1_t_1_trn_100>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_1__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_1__task_1_train_set_100'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [05:46<00:00,  1.43it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.7205882352941176,\n",
      "                 'precision': 0.765625,\n",
      "                 'recall': 0.6805555555555556,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.8319999999999999,\n",
      "                       'precision': 0.8524590163934426,\n",
      "                       'recall': 0.8125,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.6574074074074074,\n",
      "                  'precision': 0.5819672131147541,\n",
      "                  'recall': 0.7553191489361702,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.7631578947368421,\n",
      " 'macro avg': {'f1-score': 0.7366652142338417,\n",
      "               'precision': 0.7333504098360656,\n",
      "               'recall': 0.7494582348305753,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.7663016238434194,\n",
      "                  'precision': 0.7756769761730936,\n",
      "                  'recall': 0.7631578947368421,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_1_t_1_trn_100>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁█</td></tr><tr><td>f1</td><td>▁█</td></tr><tr><td>precision</td><td>▁█</td></tr><tr><td>recall</td><td>▁█</td></tr><tr><td>train_loss</td><td>▆▃█▄▆▅▂▆▃▂▁▄▁▂▂▁</td></tr><tr><td>train_mean_token_accuracy</td><td>▃▅▁▄▄▂▇▃▇▇█▅█▇▇█</td></tr><tr><td>valid_loss</td><td>█▁▃▄▅▃▂▂▄▄▂▅▂▂▁▂</td></tr><tr><td>valid_mean_token_accuracy</td><td>▁█▇▃▂▃▅▇▄▅▇▇▆▆█▆</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.76316</td></tr><tr><td>f1</td><td>0.76316</td></tr><tr><td>precision</td><td>0.76316</td></tr><tr><td>recall</td><td>0.76316</td></tr><tr><td>train_loss</td><td>0.00343</td></tr><tr><td>train_mean_token_accuracy</td><td>1.0</td></tr><tr><td>valid_loss</td><td>0.08471</td></tr><tr><td>valid_mean_token_accuracy</td><td>0.97222</td></tr></table><br/></div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">finetune_chetGPT_hatespeech_experts</strong> at: <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/498mprxe' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/498mprxe</a><br/>Synced 6 W&B file(s), 4 media file(s), 4 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>.\\wandb\\run-20250228_091810-498mprxe\\logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mark the end of the run\n",
    "wandb.finish()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chatGPT",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
